HAL Id: hal-00869038
https://hal.inria.fr/hal-00869038
Submitted on 2 Oct 2013
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Fouille de données et Apprentissage statistique
Frédéric Precioso
To cite this version:
Frédéric Precioso. Fouille de données et Apprentissage statistique. E-Santé de Proximité (ESP 2013), May 2013, Roquefort-Les-Pins, France. �hal-00869038�
PRÉSENTATION ESP
LABORATOIRE I3S –
EQUIPE KEIA
F. PRECIOSO
Fouille de données et Apprentissage statistique
Laboratoire I3S - Equipe KEIA
Enseignants-chercheurs
Nicolas Pasquier
Célia da Costa Pereira
Christel Dartigues-Pallez
Denis Pallez
Frédéric Precioso
Doctorants
Kartick Chandra MONDAL, EMMA (Erasmus Mundus Mobility for
Asia), Data mining algorithms for bioinformatics, 2009-2013
Somsack INTHASONE, EMMA (Erasmus Mundus Mobility for Asia),
Knowledge extraction techniques for biodiversity studies,
2012-2015
Romaric Pighetti, MESR, Recherche d’information par le contenu en
combinant algorithmes évolutionnaires et SVM, 2012-2015
Activités
Problèmes
Clustering : regroupement de données “similaires”
Classification : construction de modèles explicites ou implicites de classes Associations : relations de co-occurrence
Décision pour des données dynamiques et Planning Apprentissage Interactif
Méthodes
Algorithmes Evolutionaires
Machines à Vecteurs de Support Systèmes Multi-Agents
Boosting
Réseaux de Neurones artificiels
Arbres de décision et Forêts aléatoires Treillis de Galois et closure
Inférence Bayésienne Naïve …
Information retrieval from The Big Data
CBIR systems: bridging the “semantic gap”
Information mining in The Big Data
Increasing multimedia information stream… Mega-databases:
YouTube = May 2010, more videos uploaded in 60 days than filmed by the 3 biggest US majors in 60 years [1]; 20 h of video uploaded per minute in May 2009, 24 h per minute in March 2010, to reach 1 h per minute few months ago [1, 4]
Flickr = 3000 images uploaded every minute to reach 5 billions in September 2010 [1]
The Digital Universe was about 281 ExaBytes (281 Billions of GigaBytes) in 2007; it
increased 10 times since then [2]
Images and videos, captured by several billions of devices (cameras, webcams,
phones) represent the biggest part of this incredibly big amount of data [3]
[1] website-monitoring.com
[2] “digital universe.”, http://eon.businesswire.com/releases/information/digital/prweb509640.htm
[3] “major source.” , http ://www.emc.com/collateral/analyst-reports/diverse-exploding-digitaluniverse.pdf [4] http://www.onehourpersecond.com/
CBIR systems
7
Search Engine
Information mining in The Big Data
Text Mining in short messages (tweets, SMS,…)
Human action recognition
Large-scale image and video retrieval
Information mining in The Big Data
9
e-Health
Health-Autonomy
Health 2.0
Home Patient Sensors Analysis (KB system) Actuators Storage (Cloud) Home Patient Sensors Actuators Hospital / Nursery Patient Sensors Actuators Patient Sensors Actuators Physician Patient Sensors Actuators 12Ontologies describing application-specific knowledge
Large heterogeneous sensor data Patient/context data
Laboratoire I3S - Equipe KEIA
Nicolas Pasquier
Célia da Costa Pereira
Christel Dartigues-Pallez
Denis Pallez
